As data becomes increasingly abundant and complex, the observability of data also becomes more challenging. Data observability refers to an organization’s ability to ensure data quality and accessibility are maintained through the data lifecycle.
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Data observability tools can be used by an organization to create curated data sets and make it easier and more efficient to derive value from data. An organization that uses data observability tools can get actionable business insights and understand system behavior so they can predict and prevent problems in their systems such as data downtime. Read on to learn more about how data observability works and some of the best tools on the market for data observability.
Top observability tools in 2022
As one of the most popular data observability tools, Dynatrace offers an integrated platform for monitoring data on networks, applications, servers and infrastructure. It supports over 600 third-party technologies and has AI features that can be used to do root-cause analysis and UX A/B testing.
The platform offered by Dynatrace is delivered through a SaaS or an on-premises implementation. There are six plans available, including application security, full-stack monitoring, open ingestion, infrastructure monitoring, digital experience monitoring and cloud automation.
Although Dynatrace is easy to integrate, users should be aware that it does not have open-source components that allow for faster adoption. Users can try Dynatrace for free for 15 days with the paid version starting at $8 per user. The free version includes several features but has limited integration options and dashboard designs.
New Relic is a SaaS-based data observability tool with full-stack monitoring across network, mobile and browser infrastructure. It can be integrated with CodeStream, a popular collaboration platform for developers, and also offers native support for OpenTelemetry. Common use cases for the New Relic platform include log management and tracking and integrations with over 470 third-party applications and plugins.
New Relic offers good value for the price, excellent application performance monitoring and slack trace features for debugging. However, it is lacking in its UX. Some pages take considerable time to load or are difficult to view for the users. Another downside to New Relic is that it is only available on the SaaS platform, with no on-premises option.
There is a free New Relic plan available for users to determine if this is the right data observability tool for their requirements. The paid version comes with 100 GB of free data ingested per month, with $0.30/GB after that limit is reached.
Datadog is a data observability tool that offers excellent versatility for IT operations, developers, business users, security engineering, and other roles and functionalities. It offers a SaaS-based platform with a variety of features, including log management, infrastructure monitoring and performance monitoring. The dashboard views are customizable through the Playbooks feature.
Datadog is trusted by several reputable organizations around the globe, including Samsung and Shell. One of the challenges with Datadog is that it has a steep learning curve for users with some difficult navigational features. There is a free version available with two paid models: $15 per host or $1.27 per million log events.
Auvik offers a cloud-based data observability platform with real-time mapping, automated configuration backup and deep insights into network traffic. It supports over 700 technologies. The web interface of Auvik is easy to navigate, and the initial configuration is easier than most comparable data observability tools on the market.
A downside of Auvik is that it is geared toward network device monitoring rather than data-level monitoring, so it might not be suitable for every organization. The pricing for Auvik starts at $150 per month, making it more expensive than the industry standard. For large enterprises, Auvik offers custom quotes.
Grafana is an open-source analytics and visualization program that offers deep visibility of logs, metrics and traces. One of the most prominent features of Grafana is its dashboard, which consists of a group of panels that display telemetric data. Grafana applications can be implemented on-premises or in the cloud. Some of the most well-known users of Grafana include eBay, Siemens and Paypal.
The downside to using Grafana is that it can be time-consuming to configure, especially with the customizable features it offers. However, this should not be a problem after it has been set up. Another potential drawback is that it doesn’t offer native data storage, so a third-party storage solution will be required.
There are three subscription plans available for Grafana: free, pro and advanced. Pricing starts at $8 per month for one active user. The free version offers access for three team members and retains logs, metrics and traces for 14 days. Grafana also offers customized plans that include custom branding, audit logging and a customer success manager.
Splunk offers full-stack data observability with the ability to ingest telemetry data from the entire tech landscape. It supports over 2,400 apps and add-ons and has built-in AI and automation capabilities. One of the best features of Splunk is its streaming analytics, which provides near real-time analysis for rapid incident response.
A downside to Spunk is that it doesn’t offer a high level of customization for visualization. It also is more expensive than most other data observability tools on the market. Advanced features of Spunk — including the security and machine learning settings — can be challenging to set up.
This Splunk solution is available through the Spunk Cloud Platform and the on-premises Splunk Enterprise platform. There is a 14-day free trial available. The pricing of Splunk starts at $150 per ingested GB per month when billed annually. There are also custom plans based on the requirements of the client.
An introduction to observability tools
Data observability tools offer a unified and centralized platform to view and analyze data for different applications and infrastructure components. This includes continuous data monitoring and data logging, however, unlike individual monitoring tools and applications, data observability tools offer a broader scope and more functionality. These more advanced functionalities include real-time feedback from systems, proactive problem-solving, triaging of incidents and machine learning.
Important features in an observability tool
Smooth integration with existing tech stack
A strong data observability tool should be able to seamlessly connect and integrate with an existing tech stack without the need for new code or modified pipelines. Seamless integration allows for a better return on investment and minimizes disruption to business operations.
Minimal configuration requirements, no prior mapping requirements and no threshold settings are important features of a great data observability tool. To keep the enterprise tech stack running smoothly, data observability tools should form a unified system rather than a collection of poorly integrated subsystems.
Proactive notifications and problem-solving
One of the primary goals of data observability tools is that they should not only monitor data and flag any anomalies or threats but should also prevent issues from happening in the first place. An observability tool can do this by observing all data at rest without extracting it.
The tool can also prevent issues by providing valuable and actionable information about data assets, so any modifications can be made proactively and not just in response to a problem. This proactive approach is one of the pillars of data observability.
Centralized and customizable dashboards
From a user experience perspective, a data observability tool should have a central dashboard that offers a clear view of the entire system. It should also be customizable so it meets the needs of different types of users.
There should be alerts, event tracking, event logging, SLA tracking and automated issues detection features in data observability tools. A quick and easy way to test a data observability application is by manually triggering errors to check how the tool interprets the errors and how it responds.
Benefits of working with observability tools
The basic goal of data observability is to improve the performance of distributed IT systems. Conventional data monitoring techniques and tools may not be enough to handle the increasing complexity of data, which is why data observability tools are so often necessary for growing businesses. These are a few of the benefits that businesses realize soon after implementing data observability solutions:
An organization that wants to have a holistic view and understanding of data across different IT systems, all while improving operational efficiency, can benefit from using data observability tools. The deep visibility offered by data observability tools allows an organization to do a root cause analysis of most problems.
These tools are also designed to detect problems before they actually surface, allowing organizations to minimize data downtime or other types of data issues. The solutions offer other components that increase system visibility, including central dashboards, alerts and incident response.
Implementing technologies and processes that improve data team productivity has become a necessity in the business landscape of today. Data observability tools keep data pipelines running smoothly, which results in more productive teams. Not only is it important to have high quality data, but the data needs to be up-to-date and accessible with minimal downtime.
New collaboration opportunities
Another key benefit of data observability tools is that they offer improved collaboration across users. In fact, this last benefit is a key reason why so many companies are investing in data observability tools and other solutions that integrate their tech stack and simplify business processes. Recent research from TechRepublic Premium indicates that many digital transformation initiatives originate from a need to improve collaboration across departments and roles.
With the right data observability tools in place in your business, data becomes more transparent, secure and most importantly accessible for the business users who need it to succeed.